Summary of Fifo-diffusion: Generating Infinite Videos From Text Without Training, by Jihwan Kim et al.
FIFO-Diffusion: Generating Infinite Videos from Text without Training
by Jihwan Kim, Junoh Kang, Jinyoung Choi, Bohyung Han
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed FIFO-Diffusion method enables text-conditional video generation without requiring additional training for longer videos. By iteratively performing diagonal denoising, it generates frames with increasing noise levels in a queue, allowing for infinite video length capabilities. To address the training-inference gap, latent partitioning and lookahead denoising are introduced. The approach consumes a constant memory amount regardless of target video length, making it suitable for parallel inference on multiple GPUs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to create videos of any length just by giving a simple text description! This is what a new method called FIFO-Diffusion can do. It’s like having a superpower that lets you generate videos forever without needing more training data. The way it works is complex, but basically, it takes in noise and gradually cleans it up frame by frame. To make sure the results are good, some clever tricks were used to match how the model was trained with how it generates videos. This method is useful because it can process long videos on many computers at once. |
Keywords
» Artificial intelligence » Diffusion » Inference
